Overview of the SAMPL6 host-guest binding affinity prediction challenge

Andrea RizziSteven MurkliJohn N. McNeillWei YaoMatthew SullivanMichael K. Gilson, Michael W. Chiu, Lyle IsaacsBruce C. GibbDavid L. Mobley*, John D. Chodera*
* denotes co-corresponding authors
Journal of Computer-Aided Molecular Design special issue on SAMPL6, 32:937, 2018. [DOI] [bioRxiv] [GitHub]

We present an overview of the host-guest systems and participant performance for the SAMPL6 host-guest blind affinity prediction challenges, assessing how well various physical modeling approaches were able to predict ligand binding affinities for simple ligand recognition problems where receptor sampling and protonation state effects are eliminated due to the simplicity of supramolecular hosts. We find that progress is now stagnated likely due to force field limitations.

OpenPathSampling: A Python framework for path sampling simulations. II. Building and customizing path ensembles and sample schemes

David W.H. Swenson, Jan-Hendrik Prinz, Frank Noé, John D. Chodera, Peter G. Bolhuis
Journal of Chemical Theory and Computation, just accepted. [bioRxiv] [GitHub] [openpathsampling.org]

To make powerful path sampling techniques broadly accessible and efficient, we have produced a new Python framework for easily implementing path sampling strategies (such as transition path and interface sampling) in Python. This second publication describes advanced aspects of the theory and details of how to customize path ensembles.

OpenPathSampling: A Python framework for path sampling simulations. I. Basics

David W.H. Swenson, Jan-Hendrik Prinz, Frank Noé, John D. Chodera, Peter G. Bolhuis
Journal of Chemical Theory and Computation, just accepted. [bioRxiv] [GitHub] [openpathsampling.org]

To make powerful path sampling techniques broadly accessible and efficient, we have produced a new Python framework for easily implementing path sampling strategies (such as transition path and interface sampling) in Python. This first publication describes some of the theory and capabilities behind the approach.

Biomolecular simulations under realistic macroscopic salt conditions

Gregory A. Ross, Ariën S. Rustenburg, Patrick B. Grinaway, Josh Fass, and John D. Chodera
Journal of Physical Chemistry B 122:5466, 2018. [DOI] [bioRxiv] [simulation code] [results and analysis scripts]

We show how NCMC can be used to implement an efficient osmostat in molecular dynamics simulations to model realistic fluctuations in ion environments around biomolecules, and illustrate how the local salt environment around biological macromolecules can differ substantially from bulk.

OpenMM 7: Rapid Development of High Performance Algorithms for Molecular Dynamics

Peter Eastman, Jason Swails, John D. Chodera, Robert T. McGibbon, Yutong Zhao, Kyle A. Beauchamp, Lee-Ping Wang, Andrew C. Simmonett, Matthew P. Harrigan, Chaya D. Stern, Rafal P. Wiewiora, Bernard R. Brooks, Vijay S. Pande. PLoS Computational Biology 13:e1005659, 2017. [DOI] [bioRxiv] [website] [GitHub]

We describe the latest version of OpenMM, a GPU-accelerated framework for high performance molecular simulation applications.

Approaches for calculating solvation free energies and enthalpies demonstrated with an update of the FreeSolv database

Guilherme Duarte Ramos Matos, Daisy Y. Kyu, Hannes H. Loeffler, John D. Chodera, Michael R. Shirts, David Mobley
Journal of Chemical Engineering Data 62:1559, 2017. [DOI] [bioRxiv] [GitHub]

We review alchemical methods for computing solvation free energies and present an update (version 0.5) to the FreeSolv database of experimental and calculated hydration free energies of neutral compounds.